DUPLET
DUal Positron Lifetime Emission Tomography
Abstract
In recent years, there is growing evidence that the combination of metabolic- and receptor-targeted positron emission tomography (PET) imaging can optimize treatment planning for metastatic cancer patients. However, the corresponding diagnostics, performing multiple PET/CT scans, are currently not common clinical practice because of costs and availability issues of the infrastructure. The DUPLET project aims to leverage particle physics technology, state-of-the-art clinical expertise and newest radiopharmaceutical developments to combine multiple diagnostic procedures into one single scan to make optimal treatment selection viable for daily clinical practice.
This project is funded by PHRT.
People
Collaborators
Lin Zhang joined the SDSC as a senior data scientist. She completed her PhD at ETH Zurich in 2023, with a focus on simulation in medical imaging with deep learning. Before that, she obtained a bachelor degree in electrical engineering from the Technical University Munich, and a master degree in biomedical engineering from ETH Zurich. Her research interests include deep generative models, domain adaptation and applications of machine learning in healthcare.
Benjamín Béjar received a PhD in Electrical Engineering from Universidad Politécnica de Madrid in 2012. He served as a postdoctoral fellow at École Polytechnique Fédérale de Lausanne until 2017, and then he moved to Johns Hopkins University where he held a Research Faculty position until Dec. 2019. His research interests lie at the intersection of signal processing and machine learning methods, and he has worked on topics such as sparse signal recovery, time-series analysis, and computer vision methods with special emphasis on biomedical applications. Since 2021, Benjamin leads the SDSC office at the Paul Scherrer Institute in Villigen.
Fernando Perez-Cruz received a PhD. in Electrical Engineering from the Technical University of Madrid. He is Titular Professor in the Computer Science Department at ETH Zurich and Head of Machine Learning Research and AI at Spiden. He has been a member of the technical staff at Bell Labs and a Machine Learning Research Scientist at Amazon. Fernando has been a visiting professor at Princeton University under a Marie Curie Fellowship and an associate professor at University Carlos III in Madrid. He held positions at the Gatsby Unit (London), Max Planck Institute for Biological Cybernetics (Tuebingen), and BioWulf Technologies (New York). Fernando Perez-Cruz has served as Chief Data Scientist at the SDSC from 2018 to 2023, and Deputy Executive Director of the SDSC from 2022 to 2023
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Motivation
Recently, treatment of certain tumors (prostate, neuroendocrine) with radiotherapy by local internal direct irradiation has seen a wide and successful adoption. However, the diagnostics to forecast an effective treatment nowadays requires two separate PET/CT scans resulting in a higher radiation dose and costs of additional scans and/or mistreatments. The project aims to enable simultaneous dual tracer PET scans for the first time using existing clinical devices, by incorporating Positron Annihilation Spectroscopy (PAS) into the PET data processing pipeline.
Proposed Approach / Solution
In this project, SDSC contributes to the development of event selection algorithms to discriminate sources of coincidences by leveraging the isotope decay model; and advanced image reconstruction algorithms to further improve the quality of dual tracer scans. An overview of the data processing pipeline is outlined in Figure 1.
Impact
DUPLET is introducing a pioneering technology enabling the simultaneous integration of metabolic- and receptor-targeted PET scans. If successful, DUPLET holds the immediate potential to improve patient selection and deepen our understanding of various tumor responses to treatment.
Presentation
Gallery
Annexe
Additional resources
Bibliography
- Hofman, M. S. et al. “[177Lu]Lu-PSMA-617 versus Cabazitaxel in Patients with Metastatic Castration-Resistant Prostate Cancer (TheraP): A Randomised, Open-Label, Phase 2 Trial.” The Lancet 397 (2021): 797–804.
- Rohith G. “VISION trial: 177Lu-PSMA-617 for progressive metastatic castration-resistant prostate cancer.” Indian J Urol. 37 (2021): 372-373
- Ferraro, D.A., et al. “Improved oncological outcome after radical prostatectomy in patients staged with 68Ga-PSMA-11 PET: a single-center retrospective cohort comparison.” Eur J Nucl Med Mol Imaging 48 (2021): 1219–1228
- Huang, S. C., et al. "An investigation of a double-tracer technique for positron computerized tomography." Journal of Nuclear Medicine 23.9 (1982): 816-822.
- Ding, Wenxiang, et al. "Machine learning-based noninvasive quantification of single-imaging session dual-tracer 18 F-FDG and 68 Ga-DOTATATE dynamic PET-CT in oncology." IEEE transactions on medical imaging 41.2 (2021): 347-359.
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